Method and system for executing processes in operating systems

Abstract

A method and system for executing processes in an operating system is disclosed. The method may include assigning a tag Identifier (ID) and a first tree ID to each of a plurality of processes based on associated at least one attribute. The method may further include determining content patterns associated with each of the plurality of processes using a deep learning network. The method may include assigning a second tree ID to each of at least one process from the plurality of processes based on the identified content pattern. The method may further include generating a set of clusters for the plurality of processes based on the second tree ID assigned to each of the at least one process. The method may include executing each process within a cluster from the set of clusters based on execution of a single process within the cluster.

What is claimed is: 
     
       1. A method for executing processes in an operating system, the method comprising:
 assigning, by a scheduling device, a tag Identifier (ID) and a first tree ID to each of a plurality of processes based on associated at least one attribute; 
 determining, by the scheduling device, content pattern associated with each of the plurality of processes using a deep learning network; 
 assigning, by the scheduling device, a second tree ID to each of at least one process from the plurality of processes based on the identified content pattern; 
 generating, by the scheduling device, a set of clusters for the plurality of processes based on the second tree ID assigned to each of the at least one process; and 
 executing, by the scheduling device, each process within a cluster from the set of clusters based on execution of a single process within the cluster. 
 
     
     
       2. The method of  claim 1 , further comprising:
 converting a plurality of user actions into the plurality of processes; and 
 storing the plurality of processes in response to the converting, wherein each of the plurality of processes is assigned a process ID. 
 
     
     
       3. The method of  claim 2 , wherein the tree ID is same as the tag ID for a process from the plurality of processes, wherein the process forms a node in a tree represented by the tree ID. 
     
     
       4. The method of  claim 1 , wherein the at least one attribute associated with a process from the plurality of processes comprises at least one of an ID of the process, a type associated with the process, or an application associated with the process. 
     
     
       5. The method of  claim 1 , wherein the deep learning network is a Long Term Short Memory (LSTM) Network, and wherein the LSTM network uses a tan-h activation function. 
     
     
       6. The method of  claim 1 , wherein each of the plurality of processes comprises a plurality of tokens, and each of the plurality of tokens comprises at least one literal. 
     
     
       7. The method of  claim 6 , wherein determining the content pattern associated with a process from the plurality of processes comprises identifying the plurality of tokens and the at least one literal within each of the plurality of tokens by the deep learning network. 
     
     
       8. The method of  claim 7 , further comprising identifying the content pattern within the process as an existing content pattern, based on identification of the plurality of tokens and the at least one literal within each of the plurality of tokens. 
     
     
       9. The method of  claim 7 , further comprising:
 identifying the content pattern within the process as a new content pattern, based on identification of the plurality of tokens and the at least one literal within each of the plurality of tokens; and 
 training the deep learning network for the new content pattern. 
 
     
     
       10. The method of  claim 1  further comprising reusing output of the process executed from the cluster to execute each of remaining processes within the cluster. 
     
     
       11. A system for executing processes in an operating system, the system comprising:
 a processor; and 
 a memory communicatively coupled to the processor, wherein the memory stores processor instructions, which, on execution, causes the processor to:
 assign a tag Identifier (ID) and a first tree ID to each of a plurality of processes based on associated at least one attribute; 
 determine content pattern associated with each of the plurality of processes using a deep learning network; 
 assign a second tree ID to each of at least one process from the plurality of processes based on the identified content pattern; 
 generate a set of clusters for the plurality of processes based on the second tree ID assigned to each of the at least one process; and 
 execute each process within a cluster from the set of clusters based on execution of a single process within the cluster. 
 
 
     
     
       12. The system of  claim 11 , wherein the processor instructions further cause the processor to:
 convert plurality of user actions into the plurality of processes; and 
 store the plurality of processes in response to the converting, wherein each of the plurality of processes is assigned a process ID. 
 
     
     
       13. The system of  claim 11 , wherein the at least one attribute associated with a process from the plurality of processes comprises at least one of an ID of the process, a type associated with the process, or an application associated with the process. 
     
     
       14. The system of  claim 11 , wherein the deep learning network is a Long Term Short Memory (LSTM) Network, and wherein the LSTM network uses a tan-h activation function. 
     
     
       15. The system of  claim 11 , wherein each of the plurality of processes comprises a plurality of tokens, and each of the plurality of tokens comprises at least one literal. 
     
     
       16. The system of  claim 15 , wherein to determine the content pattern associated with a process from the plurality of processes, the processor instructions further cause the processor to identify the plurality of tokens and the at least one literal within each of the plurality of tokens by the deep learning network. 
     
     
       17. The system of  claim 16 , wherein the processor instructions further cause the processor to identify the content pattern within the process as an existing content pattern, based on identification of the plurality of tokens and the at least one literal within each of the plurality of tokens. 
     
     
       18. The system of  claim 17 , wherein the processor instructions further cause the processor to:
 identify the content pattern within the process as a new content pattern, based on identification of the plurality of tokens and the at least one literal within each of the plurality of tokens; and 
 train the deep learning network for the new content pattern. 
 
     
     
       19. The system of  claim 11 , wherein the processor instructions further cause the processor to reuse output of the process executed from the cluster to execute each of remaining processes within the cluster. 
     
     
       20. A non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions causing a computer comprising one or more processors to perform steps comprising:
 assigning a tag Identifier (ID) and a first tree ID to each of a plurality of processes based on associated at least one attribute; 
 determining content pattern associated with each of the plurality of processes using a deep learning network; 
 assigning a second tree ID to each of at least one process from the plurality of processes based on the identified content pattern; 
 generating a set of clusters for the plurality of processes based on the second tree ID assigned to each of the at least one process; and 
 executing each process within a cluster from the set of clusters based on execution of a single process within the cluster.